Sometimes, the lines in Jupyter notebooks are kinda hard to understand. Yet, there are a lot of materials out there online for us to explore; for this, I also learn how to solve programming problems by myself. In general, I like the courses and the instructors a lot.

Great refresher course for those who have taken courses in statistics previously, or a really good introduction for anyone new to the subject. Also a great introduction to using Python for statistical analysis. Everything is clearly laid out and easy to follow.

Great course. The materials are thoughtfully put together and paced well, and I achieved my learning objectives: a second pass through undergraduate level basic statistics and a basic idea of how to use python to do math an evaluate statistical and other data.

An excellent combination of lectures and labs. The material is extremely well taught. The mixture of lecturer styles helps to maintain interest. There is good additional content available for the deep dive topics. I found the content fresh and relevant.

This course is such a great course for beginners in Python like me. It has very helpful reading materials to aid you and great tutorials for Python using Jupyter Notebook. This made me excited to explore Python for statistical analysis in my research works.

This course provided a valuable introduction to data handling and visualizing with python. I very much valued the mix of videos, readings, and Jupiter notebook work, as well as all the pointers to additional resources for deeper dives. And the cartwheels!

Very good level of teaching, nice and clear instructions, good introduction to the topics of statistics, sampling and drawing all sorts of graphs, histograms and such. I reccomend it to anyone, no previous Python or statistical knowledge is required.

Excellent course. Well-paced, about right difficulty, and useful knowledge all around. Instructors were engaging and fun to listen to. The course managers seem to obviously care about this course, and the result is that the quality feels top-notch

An excellent course that helps to grasp the ever elusive central limit theorem and its application to simple random sampling. It provides solid foundation for learning hypothesis testing and statistical inference. Coregulations team. Great work.

A well paced stats refresher which covered the core material well and skillfully introduced current research. The fourth week was a solid introduction to sampling methodologies and inference. Looking forward to the next course in the sequence.

I particularly liked the light introduction of new concepts like methods behind confidence intervals and hypothesis tests. These were then well emphasized via numerous Jupyter Notebooks of varying levels of difficulty. Highly recommendable.

Great explanation of statistics! Pretty much exactly what I was looking for, does a great job of explaining sampling theory, and the emphasis is placed more heavily on understanding the nature/theory of statistics than the Python aspect.

I enjoy the lectures. The professor has a good speaking and teaching style which keeps me interested. Lots of concrete math examples which make it easier to understand. Very good slides which are well formulated and easy to understand

Well structured and substantive course. Gives clear examples and applications of Pandas and Numpy, as well as Matplotlib and Seaborn to analyse data in the right way. Very useful, concrete, and applicable in real life work situations

Terrific balance of information and incorporation of Python. I have programmed 40 years BUT am not in any way a programmer... this is my first ever use of Python. It was the perfect level... tough but I could hang enough to get it.

I really enjoyed taking this course. All the teachers did a great job in explaining the information in clear and understandable ways. The layout of the course was also organized which made the whole process easy. I would recommend.

That is an amazing course for someone, who has at least a little bit of experience with Python under the belt! Definitely recommend it to anyone, who would like to refresh statistical knowledge, learn how to apply it in real life.

This is a great course to start with if you are a beginner to statistics and if you know a little bit of Python. The course material is very good, concepts have been explained very well and the assignments are interesting.

Thanks for everyone involved in preparing the course content. Gained a lot of insights from this course. Really opened my eyes on the basics of statistics. Will definitely complete all the courses of this specialization!

Very informative introduction to statistics through Python course. Great instructional videos. The jupyter notebooks need to be updated with the latest Python, though, and the assignments need to be a tad more difficult

I've learned a lot from this interesting introductory course. I greatly appreciate all the hard work that the course design group members have put in, and I would like to recommend the course to my peers and friends. :)

Very informative course for beginners and this course motivates the learner to dive into statistics & python code ! jupyter notebooks are very useful which are provided in this course

Thank you for University of Michigan

I felt pretty good by taking this course. It delves into details of statistics with good hands on python programming. Acts as good foundation course and Will be helpful further gaining knowledge on in depth courses.

A great course. They example the necessary concepts with the right depth and understanding. Also, love the assignment in python, aids in using them as a template while doing a similar analysis on another dataset.

This is an excellent initial course for using python in statistical data processing and analysis of big sets of data, I appreciate the most the lectures on probability and non-probability sampling. Big Thanks.